Enhancing Email Spam Detection with Temporal Naive Bayes Classifier

Email spam detection remains a critical aspect of email security, requiring innovative approaches to combat increasingly sophisticated spamming techniques. This paper presents a novel framework for enhancing email spam detection using Temporal Naive Bayes classification. By integrating temporal dynamics into the classification process, our approach adapts to evolving spam patterns, thereby improving detection accuracy and resilience against emerging spam tactics. Our methodology leverages temporal features such as email arrival times, sender behavior patterns, and temporal distribution of keywords to capture the temporal dynamics inherent in email communication. Through the construction of a Temporal Naive Bayes classifier trained on historical email datasets, we effectively identify temporal patterns indicative of spam behavior. Experimental evaluation on diverse email datasets demonstrates the efficacy of our approach in enhancing spam detection performance compared to traditional static classifiers. The temporal-aware nature of our method enables proactive identification of evolving spam trends and dynamic adaptation to shifting email behaviors, resulting in superior detection rates while minimizing false positives. Our findings underscore the significance of temporal considerations in designing robust spam detection systems and highlight the potential of Temporal Naive Bayes classification for addressing emerging threats in email security. This research contributes to the advancement of email spam detection techniques and provides a framework for building more resilient and adaptive email security solutions.